The use of mobile fitness apps has been on the rise for the last decade and especially during the worldwide SARS-CoV-2 pandemic, which led to the closure of gyms and to reduced outdoor mobility. Fitness apps constitute a promising means for promoting more active lifestyles, although their attrition rates are remarkable and adherence to their training plans remains a challenge for developers. The aim of this project was to design an automatic classification of users into adherent and non-adherent, based on their training behavior in the first three months of app usage, for which purpose we proposed an ensemble of regression models to predict their behaviour (adherence) in the fourth month. The study was conducted using data from a total of 246 Mammoth Hunters Fitness app users. Firstly, pre-processing and clustering steps were taken in order to prepare the data and to categorize users into similar groups, taking into account the first 90 days of workout sessions. Then, an ensemble approach for regression models was used to predict user training behaviour during the fourth month, which were trained with users belonging to the same cluster. This was used to reach a conclusion regarding their adherence status, via an approach that combined affinity propagation (AP) clustering algorithm, followed by the long short-term memory (LSTM), rendering the best results (87% accuracy and 85% F1_score). This study illustrates the suggested the capacity of the system to anticipate future adherence or non-adherence, potentially opening the door to fitness app creators to pursue advanced measures aimed at reducing app attrition.

Gluten is a natural complex protein present in a variety of cereal grains, including species of wheat, barley, rye, triticale, and oat cultivars. When someone suffering from celiac disease ingests it, the immune system starts attacking its own tissues. Prevalence studies suggest that approximately 1% of the population may have gluten-related disorders during their lifetime, thus, the scientific community has tried to study different methods to detect this protein. There are multiple commercial quantitative methods for gluten detection, such as enzyme-linked immunosorbent assays (ELISAs), polymerase chain reactions, and advanced proteomic methods. ELISA-based methods are the most widely used; but despite being reliable, they also have certain constraints, such as the long periods they take to detect the protein. This study focuses on developing a novel, rapid, and budget-friendly IoT system using Near-infrared spectroscopy technology, Deep and Machine Learning algorithms to predict the presence or absence of gluten in flour samples. 12,053 samples were collected from 3 different types of flour (rye, corn, and oats) using an IoT prototype portable solution composed of a Raspberry Pi 4 and the DLPNIRNANOEVM infrared sensor. The proposed solution can collect, store, and predict new samples and is connected by using a real-time serverless architecture designed in the Amazon Web services. The results showed that the XGBoost classifier reached an Accuracy of 94.52% and an F2-score of 92.87%, whereas the Deep Neural network had an Accuracy of 91.77% and an F2-score of 96.06%. The findings also showed that it is possible to achieve high-performance results by only using the 1452–1583 nm wavelength range. The IoT prototype portable solution presented in this study not only provides a valuable contribution to the state of the art in the use of the NIRS + Artificial Intelligence in the food industry, but it also represents a first step towards the development of technologies that can improve the quality of life of people with food intolerances.

This papers presents a comparative study of three different 3D scanning modalities to acquire 3D meshes of stoma barrier rings from ostomized patients. Computerized Tomography and Structured light scanning methods were the digitization technologies studied in this research. Among the Structured Light systems, the Go!Scan 20 and the Structure Sensor were chosen as the handheld 3D scanners. Nineteen ostomized patients took part in this study, starting from the 3D scans acquisition until the printed ostomy patches validation. 3D mesh processing, mesh generation and 3D mesh comparison was carried out using commercial softwares. The results of the presented study show that the Structure Sensor, which is the low cost structured light 3D sensor, has a great potential for such applications. This study also discusses the benefits and reliability of low-cost structured light systems.

Background: Mobile health (mHealth) apps are increasingly being used to promote physical activity (PA) and can support exercise uptake and maintenance. Despite their potential, these tools face high dropout rates and inconsistent adherence, posing a significant challenge. Understanding how users engage with fitness apps is essential for improving user experience and health outcomes. Objective: This study aims to analyze user behavior patterns in the Mammoth Hunters (MH) fitness app (Mammoth Hunters SL), focusing on retention (days from registration to user’s last recorded training session), average weekly training frequency, and adherence (alignment between planned and actual training). We examined how these outcomes are influenced by sociodemographic, motivational, and other variables. Methods: This cross-sectional study involved 2771 Mammoth Hunters app users. In a subsample (n=289), training data were complemented by motivational data acquired through online surveying via an ad-hoc scale (internal consistency >0.83) based on the self-determination theory (SDT). Descriptive statistics and nonparametric tests (Kruskal-Wallis, Dunn post-hoc, and Spearman correlation) were used to assess correlation between sociodemographic, motivation, and training behavior variables. Results: Mean retention (days) was significantly higher among males than females (135 vs 109, respectively; P<.01), users in the subscription vs free plan (154 vs 81; P<.001), active or very active individuals vs inactive, midbuilt vs thin body types (132 vs 120; P=.001), and those with slightly lower BMI. Users pursuing antiaging or muscle gain goals showed longer retention than those aiming to lose weight (gain: 132, antiaging: 128, lose weight: 116; P<.001). Average weekly frequency (sessions per week) of training was statistically significantly different by sex (male: 1.9 vs female: 1.8; P=.04), body type (thin: 1.96 vs mid: 1.77; P=.04), activity level (very active: 2.05 vs inactive: 1.83; P=.04), and motivation type (extrinsic introjected motivation correlated positively: r=0.17; P<.05), but did not correlate with perceived difficulty or fitness goals. Adherence, defined as actual vs targeted training frequency, was only significantly different among body types, with thin users showing higher adherence than the midbuilt group (57% vs 52.1%; P=.02). Intrinsic motivation showed a positive correlation with retention (r=0.19; P=.002), as did identified motivation (r=0.12; P<.05). Conclusions: This study shows that retention is influenced by demographic factors, with males, subscribers, previously active, midbuilds, those aiming to gain muscle, and individuals with autonomous types (ie, intrinsic and identified) of motivation displaying greater long-term participation. These findings provide valuable preliminary insight into the complexities of exercise training behavior in apps. They suggest that training frequency, retention, and adherence do not respond to the same factors. App developers, researchers, and trainers should assess these variables separately and develop strategies accordingly.

BACKGROUND Mobile health (mHealth) apps are increasingly being used to promote physical activity (PA) and can support exercise uptake and maintenance. Despite their potential, these tools face high dropout rates and inconsistent adherence, posing a significant challenge. Understanding how users engage with fitness apps is essential for improving user experience and health outcomes. OBJECTIVE This study aims to analyze user behavior patterns in the Mammoth Hunters (MH) fitness app (Mammoth Hunters SL), focusing on retention (days from registration to user’s last recorded training session), average weekly training frequency, and adherence (alignment between planned and actual training). We examined how these outcomes are influenced by sociodemographic, motivational, and other variables. METHODS This cross-sectional study involved 2771 Mammoth Hunters app users. In a subsample (n=289), training data were complemented by motivational data acquired through online surveying via an ad-hoc scale (internal consistency >0.83) based on the self-determination theory (SDT). Descriptive statistics and nonparametric tests (Kruskal-Wallis, Dunn post-hoc, and Spearman correlation) were used to assess correlation between sociodemographic, motivation, and training behavior variables. RESULTS Mean retention (days) was significantly higher among males than females (135 vs 109, respectively; P<.01), users in the subscription vs free plan (154 vs 81; P<.001), active or very active individuals vs inactive, midbuilt vs thin body types (132 vs 120; P=.001), and those with slightly lower BMI. Users pursuing antiaging or muscle gain goals showed longer retention than those aiming to lose weight (gain: 132, antiaging: 128, lose weight: 116; P<.001). Average weekly frequency (sessions per week) of training was statistically significantly different by sex (male: 1.9 vs female: 1.8; P=.04), body type (thin: 1.96 vs mid: 1.77; P=.04), activity level (very active: 2.05 vs inactive: 1.83; P=.04), and motivation type (extrinsic introjected motivation correlated positively: r=0.17; P<.05), but did not correlate with perceived difficulty or fitness goals. Adherence, defined as actual vs targeted training frequency, was only significantly different among body types, with thin users showing higher adherence than the midbuilt group (57% vs 52.1%; P=.02). Intrinsic motivation showed a positive correlation with retention (r=0.19; P=.002), as did identified motivation (r=0.12; P<.05). CONCLUSIONS This study shows that retention is influenced by demographic factors, with males, subscribers, previously active, midbuilds, those aiming to gain muscle, and individuals with autonomous types (ie, intrinsic and identified) of motivation displaying greater long-term participation. These findings provide valuable preliminary insight into the complexities of exercise training behavior in apps. They suggest that training frequency, retention, and adherence do not respond to the same factors. App developers, researchers, and trainers should assess these variables separately and develop strategies accordingly.